Compound-specific hydrogen isotope ratios of
sedimentary n-alkanes: a new palaeoclimate proxy
Dissertation
Zur Erlangung des akademischen Grades doctor rerum naturalium
(Dr. rer. nat.)
vorgelegt dem Rat der Chemisch-Geowissenschaftlichen Fakultät der Friedrich-Schiller-
Universität Jena von Diplom-Geologe Dirk Sachse
Geboren am 24.09.1974 in Halle/Saale
Gutachter:
1. PD Dr. Gerd Gleixner (Max-Planck Institut für Biogeochemie Jena)
2. Prof. Reinhard Gaupp (Friedrich-Schiller Universität Jena)
3. Prof. John M. Hayes (Woods Hole Oceanographic Institution, Massachustetts, U.S.A.)
Datum der öffentlichen Verteidigung: 29. Juni 2005
Index
INDEX...... 3
1. INTRODUCTION...... 5
1.1. OBJECTIVES...... 7
1.2. THESIS ORGANISATION ...... 7
2. METHODS/STRATEGIES...... 9
2.1. SAMPLE SITES AND FIELD SAMPLING...... 9
18 2.2. ANALYSIS OF WATER SAMPLES FOR δ O AND δD ...... 12
2.3. SAMPLE PREPARATION, BIOMARKER IDENTIFICATION AND QUANTIFICATION...... 12
2.4. GAS CHROMATOGRAPHY TEMPERATURE CONVERSION ISOTOPE RATIO MONITORING MASS
SPECTROMETRY (GC-TC-IRMS) FOR ANALYSIS OF δD ON THE N-ALKANES ...... 13
2.5. CALCULATION OF THE ISOTOPIC FRACTIONATION ε...... 14
3. HYDROGEN ISOTOPE RATIOS OF LACUSTRINE SEDIMENTARY N-ALKANES RECORD MODERN CLIMATE VARIABILITY...... 15
3.1. INTRODUCTION ...... 15
3.2. RESULTS AND DISCUSSION...... 16 3.2.1. n-alkane concentrations ...... 16
3.2.3. δD and δ18O values of water...... 17
3.2.4. δD values of the n-alkanes ...... 19
3.2.4.1. δD values of the even carbon numbered short-chain n-alkanes (n-C12, n-C14, n-C16, n-C18, n-C20) and n-C13
and n-C15...... 20
3.2.4.2. δD values of the n-C17, n-C19, n-C21, n-C23 alkanes...... 22
3.2.4.3. δD values of the even carbon numbered medium to long-chain n-alkanes (n-C22, n-C24, n-C26, n-
C28, n-C30) ...... 24
3.2.4.4. δD values of the odd carbon numbered long-chain n-alkanes (n-C25 to n-C31) ...... 25
3.3. CONCLUSIONS...... 26
4. COMPOUND-SPECIFIC δD VALUES OF N-ALKANES FROM TERRESTRIAL PLANTS ALONG A CLIMATIC GRADIENT – IMPLICATIONS FOR THE SEDIMENTARY BIOMARKER RECORD ...... 30
4.1. INTRODUCTION ...... 30
4.2. RESULTS AND DISCUSSION...... 31
4.2.1. n-alkane concentrations of biomass ...... 31
4.2.2. δD values of n-alkanes ...... 34 4.2.2. Hydrogen isotope fractionation between source water and biomass n-alkanes ...... 37
4.2.3. Comparison with sedimentary n-alkane distributions and δD values...... 39
4.3. CONCLUSIONS...... 42
5. SEASONAL VARIATIONS IN DECIDUOUS TREE LEAF WAX N-ALKANE δD AND δ 13C VALUES: IMPLICATIONS FOR THEIR USE AS A PALAEOCLIMATE PROXY ...... 44
5.1. INTRODUCTION ...... 44
5.2. MATERIALS AND METHODS ...... 45 5.2.1. Study site and sample collection ...... 45 5.2.2. Meteorological measurements at the Hainich site ...... 46 5.2.3. Modelling leaf water isotopic enrichment...... 46
5.3. RESULTS...... 48 5.3.1. n-alkane concentrations ...... 48
5.3.2. n-alkane δD and δ13C values ...... 50
5.4. DISCUSSION...... 52 5.4.1. Seasonal variations in n-alkane concentration...... 52
5.4.2. Seasonal variations in n-alkane δD and δ13C values for Maple leaves...... 52
5.4.3. Seasonal variations in n-alkane δD and δ13C values for Beech leaves - Comparison of modelled leaf
water enrichment with n-alkane δD values ...... 54
5.4.4. Implications for the use of n-alkane δD values as a palaeoclimate proxy ...... 60
6. CONCLUDING REMARKS...... 61
7. SUMMARY...... 66
8. ZUSAMMENFASSUNG...... 68
ACKNOWLEDGEMENTS...... 71
9. REFERENCES...... 73
Introduction
1. Introduction
The reconstruction of past climate variability can provide valuable information on the causes, timing and magnitude of climate change. Since direct observational evidence for climatic changes is available only for the past 100 years, so-called palaeoclimate proxies (e.g. isotope ratios, pollen data, palaeontology, sedimentological methods etc.) can provide us with a better understanding of natural climate variability and it’s forcing mechanisms. Analysis of ice-cores from the polar regions and sediments have provided valuable information on the natural climate variability throughout the geological time. The obtained data are used to evaluate climate models, which are needed to predict possible scenarios of anthropogenic climate change. However, even with the improved knowledge of the climate system components obtained over the last years, it is evident that the interactions between the biosphere, atmosphere, lithosphere and especially the hydrosphere are, despite their generally accepted importance, still poorly understood. Research on dynamics of the water cycle in the recent geological past usually focused on the oceans, because of the availability of complete sedimentary archives. Important factors influencing terrestrial ecosystems, such as atmospheric circulation, precipitation and evapotranspiration, could not be reconstructed directly because of a lack of direct proxies. Through the use of indirect proxies, such as sedimentological evidence, fossil plants, etc., some information became available, but remains scarce. It was recognised long ago that the stable isotopes of water (protium/deuterium, oxygen16/oxygen18) can be used to follow fluxes of water in the water cycle. The ratio of these isotopes in water (expressed as relative deviations from “standard mean ocean water”, SMOW, in δD and δ18O values) holds information on the water source region, temperature and evaporation (CRAIG, 1961; GAT, 1996). Tropical ice-cores are doubtlessly the best terrestrial
18 archives for water cycle reconstruction yet, because δD and δ O values can be determined directly on the ice. Data from ice-cores extended the knowledge about atmospheric dynamics in the recent geological past (THOMPSON et al., 2003). However, ice-cores are limited to the high- altitude regions, such as the Andes, the Himalayas and partly Africa (Kilimanjaro). With the global retreat of glaciers as a first effect of Global Warming (OERLEMANS, 2005), these valuable climate archives will be severely damaged and eventually be lost. δ18O values determined on carbonate sediments and/or carbonate shells from lake sediments have been shown to record the source water δ18O value and therefore variations in climate (VON GRAFENSTEIN et al., 1999). However, interpretation of these records is not straightforward, since the fractionation of oxygen isotopes during incorporation into the shells is dependent on
5 Introduction species, water temperature and the salt content of the water, which can only be estimated from other proxies for the geological past. The development of compound-specific isotope ratio measurement on individual organic compounds, first for carbon (HAYES et al., 1990) and in the late 1990ies for hydrogen
(BURGOYNE and HAYES, 1998; HILKERT et al., 1999) opened new possibilities in tracing the carbon and hydrogen sources of individual biomarker compounds. The δD values of cellulose and also bulk plant lipids from biomass were already proven to record the source water isotopic composition (ESTEP and HOERING, 1980; NORTHFELT et al., 1981; STERNBERG, 1988). However, cellulose is not stable over geological timescales and δD values of bulk lipids vary due to different biochemical pathways of lipid biosynthesis and associated distinct isotopic fractionations. n-Alkanes belong to one biochemical group of compounds, contain only carbon- bound hydrogen, which is non-exchangeable for temperatures up to 150°C (SCHIMMELMANN et al., 1999) and hold information on their biological source. n-Alkanes are abundant in sediments from the geological past and routinely used for palaeo-ecosystem reconstructions. These compounds are of particular interest since different classes of these hydrocarbons can be used to distinguish terrestrial (i.e. higher plant) from aquatic (i.e. phytoplankton) production. Sessions et al. (1999) were the first to suggest, that different lipid classes have different hydrogen isotopic fractionations between lipid and source water (ε), explaining the observed variability in the bulk lipid fraction. Although only 3 different organisms were investigated an ε value of - 160‰ was observed for n-alkanes in laboratory studies. It was hypothesised that ε is independent of environmental parameters and only dependant on the biochemical pathway used for lipid synthesis. If this were the case, sedimentary n-alkane δD values could be used to reconstruct the isotopic composition of the organisms’ source water, and therefore variations in climate. In the following years applications of this new proxy for palaeoclimate reconstruction became already available with promising results over various geological timescales (ANDERSEN et al., 2001; HUANG et al., 2002; SACHSE et al., 2004a; XIE et al., 2000). However, a systematic study investigating the relationships between source water δD values and n-alkane δD values in natural ecosystems is still missing.
6 Introduction
1.1. Objectives The aim of this thesis was to present a systematic study, comparing modern sedimentary and biomass n-alkane δD values with climate, to offer a solid base for the application of this new and promising palaeoclimate proxy. Therefore sediments from 12 lakes throughout Europe following a gradient in meteoric water δD values from -120‰ in Northern Finland to -30‰ in Southern Italy, were recovered and analysed for n-alkane δD values. To understand the origin of the terrestrial biomarker record, the dominating biomass surrounding these lakes was also sampled. It is well known, that the leaf water in higher terrestrial plants, which is the source water plants use for biosynthesis, is enriched by evaporation processes in the plants leaves by 20-80‰ relative to the meteoric water (DAWSON and EHLERINGER, 1993; LEANEY et al., 1985; ZIEGLER, 1989). In temperate climates, as in Europe, the plant’s leaves will be deposited within lakes in autumn. So, if n-alkanes are produced de novo year-round in the leaf waxes, the autumn signal would be preserved. It is known, that the chemical composition, e.g. the relative abundance of different compounds, of leaf waxes is changing over the year (GULZ, 1994), however, no such data exist for compound- specific isotope values. Consequently, in this thesis the following questions, will be addressed:
- Do n-alkane δD values from lake sediments record the modern climate variability? - Is the isotopic fractionation of hydrogen during lipid biosynthesis (ε) in natural systems independent of environmental parameters? - Is the isotopic enrichment observed in the leaf water of plants also represented in the leaf wax n-alkanes and in sedimentary n-alkanes of terrestrial origin? - Do δD values of leaf wax n-alkanes show seasonal variations?
These issues culminate in the question: How robust are sedimentary n-alkane δD values as a proxy for changes in the water cycle over geological timescales?
1.2. Thesis Organisation The thesis is organized into 9 chapters. The following chapter 2 explains the common methodologies used in all studies (measurement of δD values on individual organic compounds) as well as the sampling sites. Chapters 3 to 5 are peer-reviewed published (Chapter
3 was published in Geochimica et Cosmochimica Acta (SACHSE et al., 2004b)) or submitted (Chapter 4 and 5) articles.
7 Introduction
Chapter 3 describes the sedimentary n-alkane δD values of 12 European lakes, chapter 4 deals with the associated plant biomass n-alkanes from the lake catchments, whereas seasonal variation in leaf wax n-alkane δD values over the 2004 growing season are discussed in Chapter 5. The thesis ends with a concluding discussion (Chapter 6), where results are synthesised, a summary (Chapter 7 and 8, in German) and the references (Chapter 9).
8 Methods/Strategies
2. Methods/Strategies
2.1. Sample Sites and Field Sampling All sampled lakes (Figure 1) are small, groundwater fed lakes with a relatively small catchment area (Table 1) and low human impact except Pääjärvi Lake in Finland, which is significantly bigger and Lago di Massaciucoli, which is subject to temporal salt-water influx from the Mediterranean Sea. Six lakes are oligotrophic, 4 lakes eutrophic, one lake is mesotrophic and one is oligo/mesotrophic. For an overview of basic limnological parameters see Table 2. The sites cover a mean annual temperature range from –2.0 °C (Naimakka, northern Sweden) to 13.7 °C (Monticchio, southern Italy). Temperature and evaporation are the main factors controlling the δD value of the meteoric water, source water for the lakes (GONFIANTINI, 1986). The resulting mean annual δD value of meteoric water on the sites cover a range from –119.0 ‰ vs. VSMOW in northern Sweden/Finland (mean annual δD from 1992-1995 in Naimakka,
Figure 1: Location of the sampling sites and mean annual δD values of meteoric water over Western Europe (IAEA, 2001)
9 Methods/Strategies
Sweden) to –36.6 ‰ vs. VSMOW in southern Italy (mean annual δD from 2000 in Bari) (IAEA, 2001) (Figure 1). Sediments were sampled in August and September 2002 using a gravity corer (HTH-Teknik, Luleå, Sweden) operated from a dismountable raft. The new sediment covering roughly the last year (usually the upper 1 to 2 cm), was collected up to 4 times in the deepest part of the lake, determined from depth maps and echo-sounding. In Lake Pääjärvi, the eastern basin, whose maximum depth is 44 m, was sampled. Water temperature-, pH-, oxygen saturation and redox profiles were determined on-site using a Multiprobe (YSI Inc., Yellow Springs, Ohio, U.S.A.), ensuring comparability of the lakes. Water samples were collected using a water sampler (Hydro-Bios Apparatebau GmbH, Kiel, Germany) and a pump from the high productivity zone of the lake (usually 1-2 m). Additionally the in- and outflow of the lake, if existent, was sampled. All water samples (1l) were filtered trough a 0.45 µm GF/F filter. The GF/F filters were frozen and later used for chlorophyll a concentration measurement (Table 2). For this purpose filters were ground and dissolved in acetone, the extinction of the extract was measured using a UV/VIS spectrometer (Helios gamma, ThermoSpectronic, Madison, WI, U.S.A.) following the procedure from Jeffrey and Humprey (1975). Phosphorus concentration was determined on-site using a field photometer (PhotoLab S12, WTW GmbH & Co. KG, Weilheim, Germany). Pooled leaf samples (about 20 shaded leaves from 2 m height) of the 2-3 dominating deciduous tree species were collected from the shores of the lakes, expect for lake Grystinge (site SOR). Additionally, two forest sites in northern Finland (SOD004 and FIN002) and one in Italy (ITA001) were sampled for leaves and stream water (representing meteoric water) if available. At the Scandinavian lakes mosses, Sphagnum species and Cladonia species make up significant portions of the vegetation, and were therefore also sampled. Since most of the lakes are situated close to a permanent research station, basic meteorological data such as temperature and precipitation data exist for the last 10 years. Long-term precipitation isotope data are taken from the IAEA GNIP database (IAEA, 2001) accessible on the internet. Additionally the Online Isotope Precipitation Calculator (OIPC, accessible at http://www.waterisotopes.org/) using the IAEA database and interpolation algorithms described in Bowen and Revenaugh (2003) was used for sites where no δD values exist. Calculated values were compared with available data and showed virtually no differences (Figure 3).
10 Methods/Strategies
Table 1: Location and basic geographic and meteorological data of the sample sites. Mean annual temperature data and mean annual precipitation data from: NAI, KEI: personal communication D. Hammarlund; SOD: Finnish Meteorological Institute (personal communication T. Laurilla); HYY: Helsinki University (personal communication T. Vesala); LAM: Lammi Biological Station (personal communication L. Arvola); SOR: wetter.com for Kopenhagen; HZM: Manderscheid station, Deutscher Wetterdienst; MAS: worldclimate.com for Pisa; MEZ: data from Valentano (RAMRATH, 1997).; LGM, LPM: Watts et al. (1996). Longitude, latitude and altitude (map datum WGS 84) were determined on-site using a handheld GPS. Water depth (e.g. of the sampling site) was determined using an echosounder; mean annual precipitation δD values were taken from the OIPC and the IAEA database (see text).
sample Lake Geographic location Altitude Mean annual Mean annual mean annual sampled code or location [m asl] temperature [°C] precipitation [mm] δD precipitation biomass
FIN002 near village of Kilpisjärvi (FIN) 69°00'16"N 20°53'52"E 496 - - -115.7 Betula pubescens NAI Oikojärvi (FIN) 68°50'55"N 21°10'50"E 463 -2.0 450 -114.9 Betula pendula -114.9 Moss -114.9 Sphagnum KEI Keitjoru (SWE) 68°40'7"N 21°30'58"E 428 -2.0 450 -114.1 Betula pubescens -114.1 Cladonia SOD003 Hirviiänkurunlampi (FIN) 67°22'44"N 26°51'3"E 178 -0.3 529 -103.9 Betula pubescens -103.9 Cladonia -103.9 Fagus sylvatica SOD007 Tunturilampi (FIN) 67°21'23"N 27°10'6"E 293 -0.3 529 -103.9 Betula pendula -103.9 Cladonia -103.9 Sphagnum SOD004 near village of Luosto (FIN) 67°10'20"N 26°51'35"E 300 - - -103.9 Betula pendula -103.9 Moss HYY Kiuvajärvi (FIN) 61°50'55"N 24°16'46"E 149 3.5 640 -92.9 Betula pendula -92.9 Moss -92.9 Sphagnum SYR Syrjänalunen (FIN) 61°11'37"N 25°8'29"E 156 - - -91.4 Myrte -91.4 Betula pendula -91.4 Moss LAM Pääjarvi (FIN) 61°3'36"N 25°5'55"E 151 3.6 619 -91.3 Betula pendula -91.3 Cladonia SOR Grystinge (DK) 55°33'11"N 11°41'42"E 30 8.6 636 -67.1 - HZM Holzmaar (GER) 50°7'10"N 6°52'44"E 436 10.5 1042 -60.1 Betula pendula -60.1 Fagus sylvatica MAS Lago di Massachiucoli (ITA) 43°50'N 10°18'49"E 18 14.1 906 -41.6 Quercus variabilis -41.6 Alnus incana -41.6 Quercus cerris ITA001 near village of Castigliano (ITA) 42°45'14"N 10°55'22"E 11 - - -38.9 Quercus robur MEZ Lago di Mezzano (ITA) 42°36'46"N 11°46'9"E 466 13.1 1030 -45.7 Quercus petraea -45.7 Carpinus betulus LGM Lago Grande di Monticchio (ITA) 40°55'58"N 15°36'10"E 674 13.7 815 -47.3 Fagus sylvatica LPM Lago Piccolo di Monticchio (ITA) 40°55'55"N 15°36'48"E 685 13.7 815 -47.3
11 Methods/Strategies
Table 2: Limnological data of the sampled lakes as determined on-site. T – temperature; DO – dissolved oxygen, TDS – total dissolved solids; chl a – chlorophyll a; PO4-P: phosphorous (as PO4 species)
sample Water Lake Catchement Secci depth water T DO saturation TDS pH chl a conc. PO4-P trophic state code depth area area surface below thermocline surface surface above thermocline [m] [km2] [km2] [m] [°C] [%] [!S] [!g/l] [!g/l]
NAI 8.4 1.5 4.5 3.65 16.5 73 27 6.82 3.6 <50 oligotrophic KEI 2.0 0.01 1.5 2.00 15.0 93 14 6.25 0.3 <50 oligotrophic SOD003 4.7 0.011 > 50 3.87 15.7 92 31 6.86 2.9 <50 oligotrophic SOD007 10.7 0.36 8.4 2.95 16.3 50 15 7.28 0.7 <50 oligotrophic HYY 13.3 0.84 30 1.56 21.1 44 28.7 6.57 5 <50 mesotrophic SYR 8.5 0.3 n.d. 4.35 20.6 92 51 7.67 0.7 <50 oligotrophic LAM 44.9 13.42 244 1.77 23.3 50 93 7.18 3.4 n.d. mesotrophic SOR 8.2 2.64 n.d 0.70 22.1 92 420 8.64 63.4 60 eutrophic HZM 24.0 0.058 2 1.40 20.3 10 246 8.5 4.9 50 eutrophic MAS 2.0 7 n.d. 0.30 25.5 n.d. 4250 8.3 7.3 <50 eutrophic MEZ 30.4 0.445 0.907 9.96 22.7 88 197 7.77 0.1 <50 oligotrophic LGM 36.7 0.405 2.37 1.40 22.5 20 440 7.25 6.8 <50 eutrophic LPM 36.0 0.08 2.37 3.50 20.6 n.d. 350 7.72 2.5 <50 oligo/meso
2.2. Analysis of water samples for δ18O and δD The isotope ratios on water were measured by on-line high-temperature (1300°C) reduction in a modified glassy carbon reactor of a high-temperature elemental analyser (TC/EA) coupled to an IRMS (DeltaplusXL, Finnigan MAT, Bremen, Germany). The average standard deviation (2σ) was 0.5 ‰ for δD values and below 0.1 ‰ for δ18O values. A detailed description of the methodology is given in Gehre et al. (2004).
2.3. Sample preparation, biomarker identification and quantification Sediment samples were ground and freeze dried. Soluble organic matter was extracted using an accelerated solvent extractor (ASE200, Dionex Corp., Sunnyvale, U.S.A.) with dichloromethane/methanol mixture (10:1) at 100°C and 2000 psi (138 bar) for 15 min in 2 cycles. Depending on the amount of organic C in the sediment samples 2-7 g of sample were used for extraction. Since biomass samples contain much higher amounts of extractable hydrocarbons than sediment samples, 1g of leaf material was sufficient for extraction. Where sufficient amount of sediment was available, the extraction procedure was performed on 2-3 independent samples. The total extract was separated on a chromatographic column made of glass (ca. 20 cm height, 2.5 cm diameter) (QVF Labortechnik GmbH, Ilmenau, Germany) filled with ca. 25 cm3
12 Methods/Strategies activated silica gel (0.040 – 0.063 mm mesh size; Merck KGaA, Darmstadt, Germany) into 3 fractions: aliphatic compounds were eluted from the column with 60 ml hexane, aromatics with 40 ml chloroform and other compounds with 40 ml methanol. The chloroform and methanol fractions were archived. Compounds of the aliphatic fraction were identified and quantified using a GC-FID (TraceGC, ThermoElectron, Rodano, Italy) by comparison to an external n- alkane standard mixture (n-C10 to n-C32). The GC was equipped with a DB5ms column (30 m, ID:0.32 mm, film thickness: 0.5 µm, Agilent, Palo Alto, U.S.A.). The PTV injector was operated in constant temperature mode at 280°C with a split ratio of 1:10. The oven was held for 2 min at 80°C then heated at 8°C/min to 320°, where the final temperature was held for 5 min. The column flow was constant at 1.8 ml/min throughout the run.
2.4. Gas Chromatography Temperature Conversion Isotope Ratio Monitoring Mass Spectrometry (GC-TC-IRMS) for analysis of δD on the n-alkanes 1 µl of the hexane dissolved aliphatic fraction was injected into a HP5890 GC (Agilent Technologies, Palo Alto, U.S.A.), equipped with a DB5ms column (30 m, ID:0.32 mm, film thickness: 0.5 µm, Agilent). The injector was operated at 280°C in splitless mode. The oven was maintained for 2 min at 60°C then heated at 6°C/min to 320°C and held for 10 min at the final temperature. The column flow was held constant at 1.7 ml/min throughout the run. One fraction of the separated compounds was transferred to an ion-trap mass spectrometer (GCQ, ThermoElectron, San Jose, U.S.A.) to monitor possible co-elution of the n-alkanes with other substances. If co-elution was evident in the MS spectrum, the δD value was not used for interpretation. The other fraction was transferred to a high-temperature conversion oven operated at 1425°C (BURGOYNE and HAYES, 1998; HILKERT et al., 1999) and quantitatively converted to H2, which was introduced into an isotope ratio mass spectrometer (IRMS) (DeltaplusXL, Finnigan MAT, Bremen, Germany) for compound-specific analysis of δD values. Each sample was independently measured 3 times. All δD values were normalized to the
VSMOW scale using a mixture of n-alkanes (n-C10 to n-C32). The δD values of the n-alkanes in the standard mixture were calibrated against international reference substances (NBS-22, IAEA- OH22). Individual n-alkanes were analysed for their δD values using the offline TC/EA (Thermal Conversion/Elemental Analyser) technique, a detailed description of the calibration and standardization procedure can be found in Sachse (2002). After the measurement of no more than 2 samples (6 GC runs), the standard mixture was
+ measured independently 3 times. If necessary a drift correction was applied. The reaction of H2
13 Methods/Strategies
+ ions with H2 molecules in the ion source of the IRMS results in the formation of H3 ions, + which also contribute to the measured mass 3. Since the amount of H3 ions formed in the source is a function of the gas pressure, a correction can be applied by determining the so-called + + H3 factor (WERNER and BRAND, 2001). A constant H3 factor therefore indicates stable ion + source conditions. The H3 factor is usually determined once a day. Achieved precision can be expressed as the average standard deviation for all measured samples and standards (3 individual measurements per sample or standard) and is given in Table 3 for the 3 measurement
+ campaigns. The H3 factor was constant during the measurement periods (Table 3).
+ Table 3: Standard deviation (2σ stdv) and H3 factor of all measured samples and standard mixtures.
+ Standard Total No. No. of Samples Total no. No. No. of H3 No. stdv mixture no. of of peaks stdv of peaks of peaks per factor stdv peaks runs per runs run run Campaign 1 (Chapter 3) 4‰ 3520 160 22 7‰ 2700 180 15 5.9 23 0.3 Campaign 2 (Chapter 4) 3‰ 437 23 19 4‰ 114 60 1-3 3.9 11 0.1 Campaign 3 (Chapter 5) 4‰ 1620 90 18 4‰ 303 93 1-8 4.0 14 0.1
The same instrumental setup as for δD measurement was used for carbon isotope analysis on the leaf lipids (Chapter 5). Here the GC was coupled to the IRMS via a GC Combustion III interface, with the oxidation oven operated at 960°C. The δ13C values were normalized to the VPDB scale using the same n-alkane mixture as for hydrogen, with predetermined TC/EA δ13C values. Average standard deviation for all peaks was 0.3‰ for the samples (n=114, 60 runs with 1-3 peaks each, depending on sample), 0.4‰ for the standard mixtures (n=323, 17 runs
with 9 peaks each, n-C14 to n-C22).
2.5. Calculation of the isotopic fractionation ε The isotopic difference between the δD value of the lake water and the δD value of the n- alkanes was calculated using equation 1.
+1000 =1000⋅ δDalkane −1 Equation 1 ε alkane / water +1000 δDwater